Calibrating Friction Functions

For home-based work trips, \(friction = travelTime^{-3}\)

For home-based other trips, \(friction = travelTime^{-0.5}\)

For non-home-based trips, \(friction = travelTime^{-2.9}\)

This produced average travel times that closely matched observed data, as shown below.

Illustrating Travel Flows

We can see that the single county which generates the most trips by far (across all three types) originate in Cleveland county. This county includes the University of Oklahoma and a large amount of residential land. The vast majority of those trips end in Oklahoma County, which contains the Downtown area. Both the desire line plot and Chord diagrams below illustrate this.

Figure 19: Desire lines plotted between counties in Oklahoma City.

Generating Travel Costs

For this step of the model we began by generating costs for our three forms of transportation: driving alone (SOV), driving with someone else (HOV), and taking transit. Although our transit skim contained fare information, we chose to instead use information from the National Transit Database as we thought it would better reflect information about transit discounts. We then made the assumption that transit cost is a function of the baseline fare cost multiplied by the number of transfers. For travel by car we used NHTS data to first find total expenditure on gas and total driving time. Using these two numbers we generated cost per minute of driving. We then used information from table 4.16 of NCHRP 716 to estimate average occupancy of vehicles for our different categories of trip types (Home Based Work, Non-Home Based, Home-Based Other). We assume that driving cost is shared equally among all car occupants.

Estimating Mode Shares

We then went into NHTS data to pull survey data about the existing mode share in OKC. We divided the modes into our three categories: HOV, SOV, and Transit.

We made the following assumptions:

We then used the NHTS survey weights to determine the percent of each of these three modes over our study area.

Choosing Models

In our process of choosing mode choice models from NCHRP 716 we went with two criteria 1. Using a Nested Logit when possible 2. Using models that we had all values for

We chose the following models:

Calculating Mode Utilities

Next we calculated utility for the different modes. This allows us to encode how much someone’s utility of a trip depends on characteristics of that trip. For example, how long someone has to wait for a bus decreases their perceived utility of transit, and how long someone has to drive decreases their utility of driving.The NCHRP models give us coefficients (how different aspects such as waiting time influence utility) but they don’t provide mode-specific constants which would tell us how the utilities of modes relate to each other. To estimate the utility of each mode we started by using the log-odds to generate total mode share for the region.

Probability and Ridership by Mode

We then used the calculated utilities in a probability model which generated the mode-share distribution for the region. By using the flow data generated by the gravity model described in the previous section, we were able to calculate the total ridership of our three different modes. Once we generated an initial estimate we were able to adjust our mode share coefficients to match the observed NHTS data, as shown in the table below:

Study Area

Figure 25: A map of the Land Run of 1895

This scenario is concerned with the area of Oklahoma City contained within the Land Run of 1895. We geolocated the map in Figure 25 to identify which census tracts would become part of the new urban center. In total, six tracts with a total area of 353 square miles were included.

Figure 26: Matching Maps of Land Seized to Census Tracts

Figure 27: The census tracts that would be returned to Kickapoo nation sovereignty

Changing the Demographics

In order to model travel behavior under the new scenario, we needed to modify the demographics of the new urban area. We chose to reference the demographic make-up of a downtown census tract44 Census Tract 1019, Oklahoma County for seeding the new area, using the same ratios of household size, composition, and car ownership, while keeping the median income the same as what those tracts already were. We also kept the same ratio of population density to employment and activity density, while scaling the population to reach our desired total of ~250,000 new residents. The dot density map in Figure 28 visualizes these demographic changes.

Figure 28: Home Based Other productions and attractions in the Land Back Scenario.

Data

The only additional data used for our scenario is a map outlining the land taken in the 1895 land run. See sidenote 3 above.

Travel Flows

To begin, we wanted to see the impact this scenario would have on the travel flows between counties. In our current model, the overwhelming majority of travel for all three trip types originates in Cleveland county and goes to Oklahoma County. Our hypothesis is that in this scenario there will be a more even distribution of trip origin and destination across the seven counties in our study area.

Congestion

One of the things we wanted to understand with this scenario was how well the current road network infrastructure could handle a dramatic increase in population. To look at this we can look at the road link vehicle volumes (the output from the trip assignment phase of our model) to see how much link vehicle volumes exceed the link capacities. Since there are no public transit routes that reach our area of interest, we will only be looking at vehicles.

Total Vehicle Miles Traveled (VMT)

We also wanted to understand the impact this would have on total Vehicle Miles Traveled. In this scenario we would be essentially positing a situation where the OKC metro area is no longer organized around the central downtown (with the University campus as a secondary attractor), but instead a more polycentric distribution. This would make the region follow a logic closer to the Raleigh-Durham-Cary research triangle, Minneapolis-St. Paul or Dallas-Fort Worth. The VMT will go up with more population, but will it go up in direct proportion to population or would it be influenced by other factors such as centralized versus polycentric development?

Flows

Figure 29: Desire lines plotted between counties in Oklahoma City.

This new set of county-level travel flows shows more evenly distributed travel and the aforementioned poly-centrism, specifically by way of increasing travel to Lincoln County. By subtracting these flow levels from the original model we can see which county-to-county desire lines are now seeing more traffic:

Figure 30: Increases in travel flow between counties in Oklahoma City.

We can then take a look at which county-to-county desire lines are experiencing less traffic. We see the greatest reduction in travel to Oklahoma County.

Figure 31: Decreases in travel flow between counties in Oklahoma City

And we can also visualize this travel in more detail through chord diagrams:

Looking at our chord diagram, we can quantitatively see that there are now significantly more trips going to and from Lincoln County. What’s interesting to note is that this includes many other trips that are being diverted from other destinations, not just new trips. In our original model for Home Based Work trips there were 408,172 trips that left Cleveland County for Oklahoma County and only 7,892 trips that left Cleveland for Lincoln County. In our Land Back model, there are only 311,942 trips leaving from Cleveland to Oklahoma and 145,365 trips leaving Cleveland for Lincoln.

Vehicle Miles Traveled and Congestion

Figure 35: Road Congestion at 5pm in Oklahoma City under the proposed Land Back scenario. Orange and Red links show where the number of vehicles (v) exceeds the capacity (c) of the road

In our original model the total population was 1.4 million. The Land Back scenario adds ~250,000 additional people to the metropolitan area, amounting to a 18.4% increase in population. In our original model, VMT was around 22 million, while in the new scenario it totals 40.8 million. This amounts to an 85% increase in VMT, clearly indicating a non-linear scaling relationship.

As a result of the increase in VMT, we would see much more congestion occurring under present infrastructure conditions. The distributions of congestion for the base model and for our scenario are shown in Figure 36. The dark purples shows where there is overlap. While both distributions peak around 1 (log VOC), this distribution shows a clear increase in links with very high levels of congestion.

Figure 36: Histogram of road segment congestion in new scenario vs current conditions

Demographics and Land Use Data

For data about the population density, income, household size, and vehicle availability of we used 5-year Sample American Community Survey (ACS) Data from 2021. For information about the land use and employment we used Longitudinal Employer Household Dynamics (LEHD). For geographic boundaries we used census data.

Road Network

To generate the Road Network we used data pulled from Open Street Map, downloaded through the service . We included in our road network all road segments labeled as motorways, motorway_links, secondary, tertiary, trunks or unclassified roads. We decided to include the unclassified roads when we realized that major roads including US-77, US-62 were not included in motorways. Adding in unclassified roads also brought back in “boulevards,” such as Oklahoma City Boulevard and North Lincoln Boulevard. Our assumption is that since the original data did not label any roads as “primary,” many roads that would have been considered primary were instead labeled as unclassified.We then began to generate a transit skim using the software Transcad.

Public Transportation Network

In this model we used General Transit Feed Specification (GTFS) data pulled from Oklahoma City EMBARK’s GTFS feed.

Road Network

All primary and secondary roads in rural areas are two-way roads even if coded as one-way roads in the OSM data. This assumption was based on cross-referencing against satellite images that indicated roads had bi-directional traffic despite being coded as one-ways in OSM. We identified rural areas by looking at the network and selecting areas that had large, mostly rectangular Transit Area Zones (TAZs). See Figure 3 for an image of primary or secondary road segments that we treated as rural two-ways.

We made the following speed assumptions: * Unclassified road speeds are 30 mph * Motorways are 60 mph * Primary are 60 mph * Secondary are 40 mph * Tertiary are 30 mph * Centroid Connectors are 15.

In our model we assumed that centroid connectors could model residential roads in each TAZ. Centroid connectors can be up to 25 miles long, but must connect to a road no more than .1 miles outside of the zone boundary. Each centroid can have up to 7 centroid connectors.

Transit Network

1. The maximum initial wait time for a public transit trip was 15 minutes.
2. The walk speed for a traveler is 2.8 miles per hour.
3. Buses move at 30 miles per hour.
4. Centroid connectors could be a maximum of 0.5 miles long.

Accessibility Metrics

We are weighting the portion of time spent waiting for a bus or train as 2.5 times the in-vehicle travel time (IVTT)

We are using a logistic decay function with an inflection point of 25 and standard deviation of 5

Census and Employment Data

Figure 37: Employment is concentrated in the downtown area. Employment information is not available for many of our Transit Analysis Zones

Figure 38: Majority of employment in OKC metro area is in the service industry.

Fig A2: Majority of employment in OKC metro area is in the service industry

Fig A3: Employment + Activity Density is greatest in downtown OKC

Fig A4: The majoirty of census tracts have fewer than 100 households with no cars. But some tracts have more than 500 households with no vehicles.

Figure 39: Vehicle Ownership Dot Density Map

Figure 40: Highest income neighborhoods are north of downtown.

Figure 41: Census tracts by income, population density, and # of adults Living with their parents.